from sklearn_benchmarks.reporting.hp_match import HpMatchReporting
import pandas as pd
pd.set_option('display.max_colwidth', None)
pd.set_option('display.max_columns', None)
pd.set_option('display.max_rows', None)

scikit-learn vs. scikit-learn-intelex (Intel® oneAPI)

reporting = HpMatchReporting(other_library="sklearnex", config="config.yml", log_scale=True)
reporting.make_report()

We assume here there is a perfect match between the hyperparameters of both librairies. For a given set of parameters and a given dataset, we compute the speedup time scikit-learn / time sklearnex. For instance, a speedup of 2 means that sklearnex is twice as fast as scikit-learn for a given set of parameters and a given dataset.

KNeighborsClassifier_brute_force

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=brute.

WARNING! Mismatch between validation scores for 14 prediction (58.33%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	e70d8e7886766d41f30889506baa1e67	2cf49829391aa7891e877f2ed070adf0	1.863516	0.117614	NaN	0.000429	0.001864	brute	-1	1	0.663	0.192837	0.009580	0.687	9.663702	9.675619	0.024
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	22d5eac4a4146149b35eae6914921514	2cf49829391aa7891e877f2ed070adf0	2.727397	0.029766	NaN	0.000293	0.002727	brute	-1	5	0.757	0.187984	0.005655	0.742	14.508632	14.515196	0.015
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	2.036001	0.034826	NaN	0.000393	0.002036	brute	1	100	0.882	0.232535	0.003636	0.875	8.755667	8.756738	0.007
8	KNeighborsClassifier_brute_force	predict	100000	1	100	16f94fc93942ff7ece860c9b0f64645f	2cf49829391aa7891e877f2ed070adf0	0.019218	0.000417	NaN	0.000042	0.019218	brute	1	100	1.000	0.007647	0.000270	0.000	2.513014	2.514576	1.000
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	2.735226	0.044376	NaN	0.000292	0.002735	brute	-1	100	0.882	0.237091	0.006604	0.875	11.536618	11.541092	0.007
11	KNeighborsClassifier_brute_force	predict	100000	1	100	2d5f2b2c02d77766434d9e5033b4a76c	2cf49829391aa7891e877f2ed070adf0	0.022868	0.002251	NaN	0.000035	0.022868	brute	-1	100	1.000	0.007983	0.001021	0.000	2.864480	2.887831	1.000
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	efac583623711d395ddae7a1e881ff68	2cf49829391aa7891e877f2ed070adf0	2.058051	0.054348	NaN	0.000389	0.002058	brute	1	5	0.757	0.194857	0.005551	0.742	10.561844	10.566128	0.015
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	b79d63bb7670c492de5c3befac58fe29	2cf49829391aa7891e877f2ed070adf0	1.134952	0.023934	NaN	0.000705	0.001135	brute	1	1	0.663	0.193584	0.004843	0.687	5.862857	5.864691	0.024
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	e70d8e7886766d41f30889506baa1e67	88843f54689e3271092f70126e1de585	1.678147	0.028749	NaN	0.000010	0.001678	brute	-1	1	0.896	0.029784	0.000870	0.967	56.344824	56.368872	0.071
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	22d5eac4a4146149b35eae6914921514	88843f54689e3271092f70126e1de585	2.584159	0.029533	NaN	0.000006	0.002584	brute	-1	5	0.922	0.030526	0.001078	0.974	84.654512	84.707312	0.052
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	16f94fc93942ff7ece860c9b0f64645f	88843f54689e3271092f70126e1de585	1.978419	0.045057	NaN	0.000008	0.001978	brute	1	100	0.929	0.070371	0.003469	0.975	28.113935	28.148073	0.046
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2d5f2b2c02d77766434d9e5033b4a76c	88843f54689e3271092f70126e1de585	2.844127	0.092454	NaN	0.000006	0.002844	brute	-1	100	0.929	0.069864	0.001380	0.975	40.709705	40.717647	0.046
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	efac583623711d395ddae7a1e881ff68	88843f54689e3271092f70126e1de585	2.103565	0.018946	NaN	0.000008	0.002104	brute	1	5	0.922	0.031732	0.001147	0.974	66.292069	66.335386	0.052
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	b79d63bb7670c492de5c3befac58fe29	88843f54689e3271092f70126e1de585	1.042292	0.014833	NaN	0.000015	0.001042	brute	1	1	0.896	0.031525	0.000521	0.967	33.062498	33.067007	0.071

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.295	0.0	-1	1	0.047	0.004	0.231	0.232	See	See
3	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.101	0.0	-1	5	0.047	0.000	0.240	0.240	See	See
6	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.214	0.0	1	100	0.046	0.001	0.243	0.243	See	See
9	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.269	0.0	-1	100	0.046	0.001	0.237	0.237	See	See
12	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.001	7.325	0.0	1	5	0.045	0.001	0.241	0.241	See	See
15	KNeighborsClassifier_brute_force	fit	100000	100000	100	0.011	0.000	7.236	0.0	1	1	0.048	0.000	0.231	0.231	See	See
18	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.346	0.0	-1	1	0.009	0.000	0.500	0.500	See	See
21	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.332	0.0	-1	5	0.009	0.000	0.524	0.525	See	See
24	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.360	0.0	1	100	0.009	0.000	0.477	0.477	See	See
27	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.004	0.000	0.366	0.0	-1	100	0.010	0.000	0.455	0.455	See	See
30	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.343	0.0	1	5	0.009	0.000	0.507	0.507	See	See
33	KNeighborsClassifier_brute_force	fit	100000	100000	2	0.005	0.000	0.318	0.0	1	1	0.009	0.000	0.548	0.548	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.864	0.118	0.000	0.002	-1	1	0.193	0.010	9.664	9.676	See	See
2	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.001	0.000	0.023	-1	1	0.007	0.000	3.050	3.051	See	See
4	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.727	0.030	0.000	0.003	-1	5	0.188	0.006	14.509	14.515	See	See
5	KNeighborsClassifier_brute_force	predict	100000	1	100	0.022	0.002	0.000	0.022	-1	5	0.008	0.000	2.965	2.965	See	See
7	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.036	0.035	0.000	0.002	1	100	0.233	0.004	8.756	8.757	See	See
8	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.000	0.000	0.019	1	100	0.008	0.000	2.513	2.515	See	See
10	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.735	0.044	0.000	0.003	-1	100	0.237	0.007	11.537	11.541	See	See
11	KNeighborsClassifier_brute_force	predict	100000	1	100	0.023	0.002	0.000	0.023	-1	100	0.008	0.001	2.864	2.888	See	See
13	KNeighborsClassifier_brute_force	predict	100000	1000	100	2.058	0.054	0.000	0.002	1	5	0.195	0.006	10.562	10.566	See	See
14	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.001	0.000	0.019	1	5	0.008	0.000	2.295	2.295	See	See
16	KNeighborsClassifier_brute_force	predict	100000	1000	100	1.135	0.024	0.001	0.001	1	1	0.194	0.005	5.863	5.865	See	See
17	KNeighborsClassifier_brute_force	predict	100000	1	100	0.019	0.001	0.000	0.019	1	1	0.008	0.001	2.392	2.401	See	See
19	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.678	0.029	0.000	0.002	-1	1	0.030	0.001	56.345	56.369	See	See
20	KNeighborsClassifier_brute_force	predict	100000	1	2	0.005	0.002	0.000	0.005	-1	1	0.001	0.000	6.771	6.786	See	See
22	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.584	0.030	0.000	0.003	-1	5	0.031	0.001	84.655	84.707	See	See
23	KNeighborsClassifier_brute_force	predict	100000	1	2	0.010	0.005	0.000	0.010	-1	5	0.001	0.000	14.631	14.699	See	See
25	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.978	0.045	0.000	0.002	1	100	0.070	0.003	28.114	28.148	See	See
26	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	100	0.001	0.000	3.661	3.685	See	See
28	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.844	0.092	0.000	0.003	-1	100	0.070	0.001	40.710	40.718	See	See
29	KNeighborsClassifier_brute_force	predict	100000	1	2	0.007	0.002	0.000	0.007	-1	100	0.001	0.000	8.823	8.849	See	See
31	KNeighborsClassifier_brute_force	predict	100000	1000	2	2.104	0.019	0.000	0.002	1	5	0.032	0.001	66.292	66.335	See	See
32	KNeighborsClassifier_brute_force	predict	100000	1	2	0.003	0.000	0.000	0.003	1	5	0.001	0.000	4.912	4.934	See	See
34	KNeighborsClassifier_brute_force	predict	100000	1000	2	1.042	0.015	0.000	0.001	1	1	0.032	0.001	33.062	33.067	See	See
35	KNeighborsClassifier_brute_force	predict	100000	1	2	0.002	0.000	0.000	0.002	1	1	0.001	0.000	2.602	2.616	See	See

KNeighborsClassifier_kd_tree

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=kd_tree.

WARNING! Mismatch between validation scores for 12 prediction (50.0%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	n_jobs	n_neighbors	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	fe8267e25dadb302ed498e50fabf7ef4	1eb6cf1a720a225efb91df0b529b0510	0.826547	1.011824	NaN	0.000097	0.000827	kd_tree	-1	1	0.929	0.102737	0.002954	0.910	8.045311	8.048637	0.019
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	dc74b969bf622bc24ba3bc62c980983b	1eb6cf1a720a225efb91df0b529b0510	1.012278	0.332108	NaN	0.000079	0.001012	kd_tree	-1	5	0.946	0.179715	0.001791	0.941	5.632698	5.632978	0.005
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	41accfcdcd5c50784bedf6164b63de99	1eb6cf1a720a225efb91df0b529b0510	5.421563	0.349170	NaN	0.000015	0.005422	kd_tree	1	100	0.951	0.552325	0.021537	0.940	9.815886	9.823346	0.011
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	022f7445d43bb1dbc24dc3106c03cb93	1eb6cf1a720a225efb91df0b529b0510	3.141040	0.143311	NaN	0.000025	0.003141	kd_tree	-1	100	0.951	0.551531	0.006665	0.940	5.695124	5.695539	0.011
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	84622ba45e941db642965553529e1941	1eb6cf1a720a225efb91df0b529b0510	1.625557	0.200726	NaN	0.000049	0.001626	kd_tree	1	5	0.946	0.183751	0.005048	0.941	8.846533	8.849871	0.005
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	a9455565db1d8e052a783317c99744ff	1eb6cf1a720a225efb91df0b529b0510	0.859269	0.173876	NaN	0.000093	0.000859	kd_tree	1	1	0.929	0.098223	0.001575	0.910	8.748185	8.749310	0.019
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	fe8267e25dadb302ed498e50fabf7ef4	edf7cb014c2c0910e1c3aaaf8ae633f2	0.027149	0.012476	NaN	0.000589	0.000027	kd_tree	-1	1	0.891	0.000456	0.000050	0.879	59.584910	59.948561	0.012
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	dc74b969bf622bc24ba3bc62c980983b	edf7cb014c2c0910e1c3aaaf8ae633f2	0.024406	0.001728	NaN	0.000656	0.000024	kd_tree	-1	5	0.911	0.000726	0.000034	0.905	33.609957	33.646873	0.006
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	41accfcdcd5c50784bedf6164b63de99	edf7cb014c2c0910e1c3aaaf8ae633f2	0.038928	0.008036	NaN	0.000411	0.000039	kd_tree	1	100	0.894	0.005343	0.000023	0.917	7.285176	7.285242	0.023
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	022f7445d43bb1dbc24dc3106c03cb93	edf7cb014c2c0910e1c3aaaf8ae633f2	0.037684	0.004508	NaN	0.000425	0.000038	kd_tree	-1	100	0.894	0.006200	0.001436	0.917	6.078173	6.239117	0.023
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	84622ba45e941db642965553529e1941	edf7cb014c2c0910e1c3aaaf8ae633f2	0.022591	0.000297	NaN	0.000708	0.000023	kd_tree	1	5	0.911	0.000747	0.000024	0.905	30.248385	30.264077	0.006
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	a9455565db1d8e052a783317c99744ff	edf7cb014c2c0910e1c3aaaf8ae633f2	0.020113	0.000474	NaN	0.000795	0.000020	kd_tree	1	1	0.891	0.000444	0.000025	0.879	45.251136	45.320874	0.012

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	2.986	0.098	0.027	0.0	-1	1	0.749	0.037	3.985	3.990	See	See
3	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.624	0.073	0.022	0.0	-1	5	0.737	0.016	4.915	4.916	See	See
6	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.715	0.080	0.022	0.0	1	100	0.719	0.008	5.164	5.165	See	See
9	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.796	0.114	0.021	0.0	-1	100	0.738	0.018	5.143	5.144	See	See
12	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.780	0.060	0.021	0.0	1	5	0.714	0.014	5.294	5.295	See	See
15	KNeighborsClassifier_kd_tree	fit	1000000	1000000	10	3.727	0.104	0.021	0.0	1	1	0.727	0.016	5.124	5.126	See	See
18	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.001	0.021	0.0	-1	1	0.003	0.001	0.253	0.280	See	See
21	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.027	0.0	-1	5	0.001	0.000	0.501	0.537	See	See
24	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.028	0.0	1	100	0.001	0.001	0.438	0.528	See	See
27	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.028	0.0	-1	100	0.001	0.000	0.594	0.595	See	See
30	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.028	0.0	1	5	0.001	0.000	0.592	0.593	See	See
33	KNeighborsClassifier_kd_tree	fit	1000	1000	2	0.001	0.000	0.028	0.0	1	1	0.001	0.000	0.604	0.605	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	n_jobs	n_neighbors	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.827	1.012	0.000	0.001	-1	1	0.103	0.003	8.045	8.049	See	See
2	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	1	0.000	0.000	10.454	10.887	See	See
4	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.012	0.332	0.000	0.001	-1	5	0.180	0.002	5.633	5.633	See	See
5	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.003	0.001	0.000	0.003	-1	5	0.000	0.000	7.697	7.913	See	See
7	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	5.422	0.349	0.000	0.005	1	100	0.552	0.022	9.816	9.823	See	See
8	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.004	0.001	0.000	0.004	1	100	0.001	0.000	5.044	5.203	See	See
10	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	3.141	0.143	0.000	0.003	-1	100	0.552	0.007	5.695	5.696	See	See
11	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.005	0.001	0.000	0.005	-1	100	0.001	0.000	6.956	7.125	See	See
13	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	1.626	0.201	0.000	0.002	1	5	0.184	0.005	8.847	8.850	See	See
14	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.002	0.001	0.000	0.002	1	5	0.000	0.000	4.292	4.431	See	See
16	KNeighborsClassifier_kd_tree	predict	1000000	1000	10	0.859	0.174	0.000	0.001	1	1	0.098	0.002	8.748	8.749	See	See
17	KNeighborsClassifier_kd_tree	predict	1000000	1	10	0.001	0.000	0.000	0.001	1	1	0.000	0.000	4.019	4.153	See	See
19	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.027	0.012	0.001	0.000	-1	1	0.000	0.000	59.585	59.949	See	See
20	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	1	0.000	0.000	25.435	26.520	See	See
22	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.024	0.002	0.001	0.000	-1	5	0.001	0.000	33.610	33.647	See	See
23	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	5	0.000	0.000	22.209	22.959	See	See
25	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.039	0.008	0.000	0.000	1	100	0.005	0.000	7.285	7.285	See	See
26	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	100	0.000	0.000	5.709	5.948	See	See
28	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.038	0.005	0.000	0.000	-1	100	0.006	0.001	6.078	6.239	See	See
29	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.002	0.000	0.000	0.002	-1	100	0.000	0.000	21.252	22.105	See	See
31	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.023	0.000	0.001	0.000	1	5	0.001	0.000	30.248	30.264	See	See
32	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	5	0.000	0.000	6.669	6.937	See	See
34	KNeighborsClassifier_kd_tree	predict	1000	1000	2	0.020	0.000	0.001	0.000	1	1	0.000	0.000	45.251	45.321	See	See
35	KNeighborsClassifier_kd_tree	predict	1000	1	2	0.001	0.000	0.000	0.001	1	1	0.000	0.000	6.275	6.505	See	See

KMeans_tall

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=3, max_iter=30, n_init=1, tol=1e-16.

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_tall	fit	1000000	1000000	2	0.544	0.075	30	0.029	0.0	random	0.434	0.029	1.252	1.255	See	See
3	KMeans_tall	fit	1000000	1000000	2	0.596	0.022	30	0.027	0.0	k-means++	0.461	0.024	1.293	1.295	See	See
6	KMeans_tall	fit	1000000	1000000	100	5.705	0.230	30	0.140	0.0	random	2.630	0.040	2.170	2.170	See	See
9	KMeans_tall	fit	1000000	1000000	100	5.881	0.043	30	0.136	0.0	k-means++	2.735	0.021	2.150	2.150	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_tall	predict	1000000	1000	2	0.002	0.000	30	0.009	0.000	random	0.0	0.0	8.155	13.885	See	See
2	KMeans_tall	predict	1000000	1	2	0.002	0.000	30	0.000	0.002	random	0.0	0.0	10.176	14.233	See	See
4	KMeans_tall	predict	1000000	1000	2	0.002	0.001	30	0.008	0.000	k-means++	0.0	0.0	11.271	12.264	See	See
5	KMeans_tall	predict	1000000	1	2	0.001	0.000	30	0.000	0.001	k-means++	0.0	0.0	12.540	13.137	See	See
7	KMeans_tall	predict	1000000	1000	100	0.002	0.001	30	0.403	0.000	random	0.0	0.0	7.153	7.459	See	See
8	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	random	0.0	0.0	11.868	12.192	See	See
10	KMeans_tall	predict	1000000	1000	100	0.002	0.000	30	0.418	0.000	k-means++	0.0	0.0	7.080	7.410	See	See
11	KMeans_tall	predict	1000000	1	100	0.001	0.000	30	0.001	0.001	k-means++	0.0	0.0	11.185	11.438	See	See

KMeans_short

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: algorithm=full, n_clusters=300, max_iter=20, n_init=1, tol=1e-16.

WARNING! Mismatch between validation scores for 4 prediction (50.0%). The observed differences can be found in the diff_adjusted_rand_scores column. The chosen difference threshold is 0.001 for adjusted_rand_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	algorithm	init	max_iter	n_clusters	n_init	tol	adjusted_rand_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	adjusted_rand_score_sklearnex	speedup	std_speedup	diff_adjusted_rand_scores
1	KMeans_short	predict	10000	1000	2	3db9c030f488d0e8ff350ae449da4627	058b7e3842b587a5c675518f0706f5ee	0.002067	0.000133	20	0.007741	0.000002	full	random	20	300	1	1.000000e-16	0.000126	0.000472	0.000043	-0.000965	4.375769	4.393852	0.001090
4	KMeans_short	predict	10000	1000	2	ac9b8cec4749455c0d1cd1a92c150716	058b7e3842b587a5c675518f0706f5ee	0.001914	0.000158	20	0.008361	0.000002	full	k-means++	20	300	1	1.000000e-16	0.001245	0.000452	0.000037	-0.000750	4.236402	4.250681	0.001995
7	KMeans_short	predict	10000	1000	100	3db9c030f488d0e8ff350ae449da4627	7f85b913f395ec54101a6738ea63a9a7	0.002658	0.000244	20	0.300976	0.000003	full	random	20	300	1	1.000000e-16	0.278733	0.001114	0.000073	0.293767	2.385801	2.390878	0.015034
10	KMeans_short	predict	10000	1000	100	ac9b8cec4749455c0d1cd1a92c150716	7f85b913f395ec54101a6738ea63a9a7	0.002692	0.000136	20	0.297189	0.000003	full	k-means++	20	300	1	1.000000e-16	0.317011	0.001046	0.000070	0.256968	2.573105	2.578858	0.060044

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	KMeans_short	fit	10000	10000	2	0.086	0.003	20	0.002	0.0	random	0.029	0.003	2.945	2.961	See	See
3	KMeans_short	fit	10000	10000	2	0.240	0.007	20	0.001	0.0	k-means++	0.089	0.002	2.708	2.708	See	See
6	KMeans_short	fit	10000	10000	100	0.214	0.008	20	0.037	0.0	random	0.119	0.005	1.803	1.804	See	See
9	KMeans_short	fit	10000	10000	100	0.653	0.017	20	0.012	0.0	k-means++	0.330	0.010	1.978	1.978	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	init	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.008	0.000	random	0.000	0.0	4.376	4.394	See	See
2	KMeans_short	predict	10000	1	2	0.001	0.0	20	0.000	0.001	random	0.000	0.0	12.378	12.861	See	See
4	KMeans_short	predict	10000	1000	2	0.002	0.0	20	0.008	0.000	k-means++	0.000	0.0	4.236	4.251	See	See
5	KMeans_short	predict	10000	1	2	0.002	0.0	20	0.000	0.002	k-means++	0.000	0.0	12.899	13.117	See	See
7	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.301	0.000	random	0.001	0.0	2.386	2.391	See	See
8	KMeans_short	predict	10000	1	100	0.001	0.0	20	0.001	0.001	random	0.000	0.0	9.017	9.095	See	See
10	KMeans_short	predict	10000	1000	100	0.003	0.0	20	0.297	0.000	k-means++	0.001	0.0	2.573	2.579	See	See
11	KMeans_short	predict	10000	1	100	0.002	0.0	20	0.000	0.002	k-means++	0.000	0.0	10.381	10.535	See	See

LogisticRegression

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: penalty=l2, dual=False, tol=0.0001, C=1.0, fit_intercept=True, intercept_scaling=1, class_weight=nan, random_state=nan, solver=lbfgs, max_iter=100, multi_class=auto, verbose=0, warm_start=False, n_jobs=nan, l1_ratio=nan.

WARNING! Mismatch between validation scores for 2 prediction (50.0%). The observed differences can be found in the diff_accuracy_scores column. The chosen difference threshold is 0.001 for accuracy_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	C	class_weight	dual	fit_intercept	intercept_scaling	l1_ratio	max_iter	multi_class	n_jobs	penalty	random_state	solver	tol	verbose	warm_start	accuracy_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	accuracy_score_sklearnex	speedup	std_speedup	diff_accuracy_scores
1	LogisticRegression	predict	1000000	1000	100	f11be2529b0a77bc8fb5ee8de141e6b2	09c36ed6521e03974d0c134895f56c01	0.000396	0.000384	[20]	2.019242	3.961883e-07	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.56	0.000710	0.001079	0.55	0.558066	1.015390	0.01
4	LogisticRegression	predict	1000	100	10000	f11be2529b0a77bc8fb5ee8de141e6b2	7daecaea01e5baa8c862f264e3c1c3b0	0.001701	0.000196	[26]	4.702119	1.701361e-05	1.0	NaN	False	True	1	NaN	100	auto	NaN	l2	NaN	lbfgs	0.0001	0	False	0.35	0.005957	0.000676	0.28	0.285612	0.287444	0.07

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	LogisticRegression	fit	1000000	1000000	100	11.001	0.341	[20]	0.073	0.000	1.852	0.034	5.939	5.940	See	See
3	LogisticRegression	fit	1000	1000	10000	0.917	0.587	[26]	0.087	0.001	0.755	0.039	1.214	1.216	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	LogisticRegression	predict	1000000	1000	100	0.000	0.0	[20]	2.019	0.0	0.001	0.001	0.558	1.015	See	See
2	LogisticRegression	predict	1000000	1	100	0.000	0.0	[20]	0.013	0.0	0.000	0.000	0.389	0.394	See	See
4	LogisticRegression	predict	1000	100	10000	0.002	0.0	[26]	4.702	0.0	0.006	0.001	0.286	0.287	See	See
5	LogisticRegression	predict	1000	1	10000	0.000	0.0	[26]	1.064	0.0	0.002	0.000	0.047	0.048	See	See

Ridge

scikit-learn-intelex (2021.20210705.191215) vs. scikit-learn (1.0.dev0)

Speedup barplots

All estimators share the following parameters: alpha=1.0, fit_intercept=True, normalize=deprecated, copy_X=True, max_iter=nan, tol=0.001, solver=auto, random_state=nan.

WARNING! Mismatch between validation scores for 1 prediction (25.0%). The observed differences can be found in the diff_r2_scores column. The chosen difference threshold is 0.001 for r2_score. See details in the dataframe below.

	estimator	function	n_samples_train	n_samples	n_features	parameters_digest	dataset_digest	mean_duration_sklearn	std_duration_sklearn	n_iter	iteration_throughput	latency	alpha	copy_X	fit_intercept	max_iter	normalize	random_state	solver	tol	r2_score_sklearn	mean_duration_sklearnex	std_duration_sklearnex	r2_score_sklearnex	speedup	std_speedup	diff_r2_scores
1	Ridge	predict	1000	1000	10000	a70ae8dc5c059ec88cf27c61016aee2a	14fae294d530397144c65ba3209bf125	0.009528	0.000265	NaN	8.396224	0.00001	1.0	True	True	NaN	deprecated	NaN	auto	0.001	0.082567	0.015728	0.000434	0.122191	0.605813	0.606044	0.039624

Raw results

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
0	Ridge	fit	1000	1000	10000	0.178	0.003	0.449	0.0	0.183	0.003	0.972	0.972	See	See
3	Ridge	fit	1000000	1000000	100	1.102	0.044	0.726	0.0	0.325	0.274	3.391	4.437	See	See

	estimator	function	n_samples_train	n_samples	n_features	mean_duration_sklearn	std_duration_sklearn	iteration_throughput	latency	mean_duration_sklearnex	std_duration_sklearnex	speedup	std_speedup	sklearn_profiling	sklearnex_profiling
1	Ridge	predict	1000	1000	10000	0.01	0.0	8.396	0.0	0.016	0.0	0.606	0.606	See	See
2	Ridge	predict	1000	1	10000	0.00	0.0	1.217	0.0	0.000	0.0	0.681	0.729	See	See
4	Ridge	predict	1000000	1000	100	0.00	0.0	5.860	0.0	0.000	0.0	0.430	0.646	See	See
5	Ridge	predict	1000000	1	100	0.00	0.0	0.015	0.0	0.000	0.0	0.606	0.634	See	See